{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T21:37:23Z","timestamp":1787866643279,"version":"build-2784847793"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2025J011049"],"award-info":[{"award-number":["2025J011049"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Lower limb exoskeleton systems require accurate recognition of the wearer\u2019s movement intentions prior to action execution in order to achieve natural and smooth human\u2013machine interaction. Surface electromyography (sEMG) signals can reflect neural activation of muscles before movement onset, making them a key physiological source for movement intention recognition. To improve sEMG-based recognition performance, this study proposes an innovative deep learning framework, IFIANet. First, a CNN\u2013TCN-based spatiotemporal feature learning network is constructed, which efficiently models and represents multi-scale temporal\u2013frequency features while effectively reducing model parameter complexity. Second, an IFIA (Frequency-Informed Integration Attention) module is designed to incorporate global frequency information, compensating for frequency components potentially lost during time\u2013frequency transformations, thereby enhancing the discriminability and robustness of temporal\u2013frequency features. Extensive ablation and comparative experiments on the publicly available MyPredict1 dataset demonstrate that the proposed framework maintains stable performance across different prediction times and achieves over 82% average recognition accuracy in within-experiments involving nine participants. The results indicate that IFIANet effectively fuses local temporal\u2013frequency features with global frequency priors, providing an efficient and reliable approach for sEMG-based movement intention recognition and intelligent control of exoskeleton systems.<\/jats:p>","DOI":"10.3390\/s26010169","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:50:42Z","timestamp":1766969442000},"page":"169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["IFIANet: Frequency Attention Network for Time\u2013Frequency in sEMG-Based Motion Intent Recognition"],"prefix":"10.3390","volume":"26","author":[{"given":"Gang","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry Univeristy, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiankai","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry Univeristy, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry Univeristy, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4339-8464","authenticated-orcid":false,"given":"Heming","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Sanming University, Sanming 365004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayang","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry Univeristy, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longtao","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry Univeristy, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1109\/TNSRE.2021.3087135","article-title":"A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion","volume":"29","author":"Liu","year":"2021","journal-title":"IEEE Trans. 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